lastvsales

vsales <- read.csv("C:/Users/kaliy/OneDrive/Desktop/vsales.csv")
##vsales

Changing year from a string to a date

library(tidyverse)

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## ✔ purrr     1.0.1     
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Year <- as.Date(as.character(vsales$Year), format = "%Y")

Year <- year(Year)
view(Year)
newvsales <- vsales%>%
  select(Rank, Name, Year, Genre, Platform,NA_Sales, EU_Sales, JP_Sales, Other_Sales, Global_Sales, Platform, Publisher )%>%
  mutate(Year= Year) 

library(ggplot2)
library("patchwork")

NAS <- newvsales%>%
  select(Genre, NA_Sales)%>%
  mutate(Genre = fct_lump(Genre,9))%>%
  ggplot(aes(Genre, NA_Sales))+
  geom_bar(stat = "identity") +
  coord_flip()
  
JP <-  newvsales%>%
    select(Genre, JP_Sales)%>%
    mutate(Genre = fct_lump(Genre,9))%>%
    ggplot(aes(Genre, JP_Sales))+
    geom_bar(stat = "identity")+
    coord_flip()
  
 EU <-  newvsales%>%
    select(Genre, EU_Sales)%>%
    mutate(Genre = fct_lump(Genre,9))%>%
    ggplot(aes(Genre, EU_Sales))+
    geom_bar(stat = "identity")+
    coord_flip()

Other<-  newvsales%>%
    select(Genre, Other_Sales)%>%
    mutate(Genre = fct_lump(Genre,9))%>%
    ggplot(aes(Genre, Other_Sales))+
    geom_bar(stat = "identity")+
    coord_flip()
   
 NAS + EU+ JP + Other

Change of sales over time

library(patchwork)

c <- newvsales%>%
  group_by(Year)%>%
  summarise(sumNA = sum(NA_Sales))

p <- newvsales%>%
  group_by(Year)%>%
  summarise(sumEU = sum(EU_Sales))

f <- newvsales%>%
  group_by(Year)%>%
  summarise(sumJP = sum(JP_Sales))

a <- left_join(c, p , by ="Year")
g <- left_join(a ,f, by= "Year")

ggplot( g, aes(x =Year))+
  geom_point(aes(y = sumNA, color = "NA"))+
  geom_point(aes(y = sumEU, color = "EU")) +
  geom_point(aes(y = sumJP, color = "JP"))+
  coord_flip()

newvsales%>%
  group_by(Publisher)%>%
  summarise(pubna= sum(NA_Sales))%>%
  arrange(desc(pubna))%>%
  top_n(7, pubna)%>%
  ggplot()+
  aes(Publisher, pubna)+
  geom_bar(stat = "identity")+
  coord_flip()

allNA <- sum(newvsales$NA_Sales)

newvsales%>%
  group_by(Publisher)%>%
  summarise(pubna= sum(NA_Sales))%>%
  top_n(10, pubna)%>%
  mutate(per=( (pubna/allNA) *100))%>%
  arrange(desc(per))%>%
  ggplot( aes(x = "", y =per , fill = Publisher)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y" , start = 0)+
  geom_text(aes(x=1, y = cumsum(per)-per/2, label=round(per))) + 
  labs(x =NULL, y = NULL, fill = NULL)

library("plotly")

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

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##     layout

library(dplyr)
library("gapminder")

plot1 <- ggplot(data = newvsales, aes(x = Platform, y = NA_Sales)) +
  geom_point()+
  coord_flip()

ggplotly(plot1)